18 citations · 70 across the 30 of their papers we have counts for
6 papers · 1 filter
AIM 2019 Challenge on Image Demoireing: Methods and Results
Shanxin Yuan, Radu Timofte, Gregory Slabaugh +25
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper desc…
AIM 2019 Challenge on Image Demoireing: Dataset and Study
Shanxin Yuan, Radu Timofte, Gregory Slabaugh +1
This paper introduces a novel dataset, called LCDMoire, which was created for the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) wo…
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana +5
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct trai…
Deep Dexterous Grasping of Novel Objects from a Single View
Umit Rusen Aktas, Chao Zhao, Marek Kopicki +2
Dexterous grasping of a novel object given a single view is an open problem. This paper makes several contributions to its solution. First, we present a simulator for generating an…
Learning Manipulation under Physics Constraints with Visual Perception
Wenbin Li, Aleš Leonardis, Jeannette Bohg +1
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing nov…
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Domen Tabernik, Matej Kristan, Aleš Leonardis
Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be man…